Porsche Price (2)

• Go to RStudio Cloud and open Porsche Price (2)

Steps for modeling

Steps for modeling

Conditions for simple linear regression

• Linearity
• Zero Mean
• Constant Variance
• Independence
• Random
• Normality

Conditions for simple linear regression

• Linearity
• Zero Mean
• Constant Variance
• Independence
• Random
• Normality

Conditions for simple linear regression

• Linearity
• Zero Mean
• Constant Variance
• Independence
• Random
• Normality

Linearity & Constant Variance

Linearity & Constant Variance

Residuals versus fits plot

Linearity & Constant Variance

Linearity & Constant Variance

Residuals versus fits plot: What are we looking for?

• random variation above and below 0
• no apparent "patterns"
• the width of the "band" of points is relatively constant

Linearity & Constant Variance

Linearity & Constant Variance

Linearity & Constant Variance

Linearity & Constant Variance

Conditions for simple linear regression

• Linearity
• Zero Mean
• Constant Variance
• Independence
• Random
• Normality

Conditions for simple linear regression

• Linearity
• Zero Mean
• Constant Variance
• Independence
• Random
• Normality

Conditions for simple linear regression

• Linearity
• Zero Mean
• Constant Variance
• Independence
• Random
• Normality

Normality

• Histogram
• Normal quantile plot

Normality

Histogram

Normality

Histogram

Normality

Histogram

Normality

Normal quantile plot

Normality

Normal quantile plot

Normality

Normal quantile plot

Plot the data with a line

Plot the data with a line

y_hat <- lm(Weight ~ WingLength, data = Sparrows) %>% 
  predict()
Sparrows <- Sparrows %>%
  mutate(y_hat = y_hat,
         residuals = Weight - y_hat)

Plot the data with a line

Residuals vs Fits plot

ggplot(Sparrows, aes(x = y_hat, y = residuals)) + 
  geom_point() + 
  geom_hline(yintercept = 0) +
  labs(title = "Residuals versus fits", 
       subtitle = "The relationship between WingLength and Weight for Sparrows",
       x = "y hat")

Residuals vs Fits plot

ggplot(Sparrows, aes(x = y_hat, y = residuals)) + 
  geom_point() + 
  geom_hline(yintercept = 0) +
  labs(title = "Residuals versus fits", 
       subtitle = "The relationship between WingLength and Weight for Sparrows",
       x = "y hat")

Histogram of the residuals

ggplot(Sparrows, aes(residuals)) +
  geom_histogram(binwidth = 0.5)

Normal quantile plot

ggplot(Sparrows, aes(sample = residuals)) + 
  geom_qq() +
  geom_qq_line()

Normal quantile plot

ggplot(Sparrows, aes(sample = residuals)) +
  geom_qq() +
  geom_qq_line()

Normal quantile plot

ggplot(Sparrows, aes(sample = residuals)) + 
  geom_qq()

Normal quantile plot

ggplot(Sparrows, aes(sample = residuals)) + 
  geom_qq() +
  geom_qq_line()

Porsche Price (2)

• Go to RStudio Cloud and open Porsche Price (2)
• For each question you work on, set the eval chunk option to TRUE and knit